double loglikelihoodForOneCluster = bestMixtureModel.loglikelihood(m_Values, m_Weights); double bestNormalizedEntropy = 1; for (int i = 2; i <= m_MaxNumComponents; i++) { MM mixtureModel = buildModel(i, m_Values, m_Weights); double loglikelihood = mixtureModel.loglikelihood(m_Values, m_Weights); if (loglikelihood < loglikelihoodForOneCluster) {
double loglikelihoodForOneCluster = bestMixtureModel.loglikelihood(m_Values, m_Weights); double bestNormalizedEntropy = 1; for (int i = 2; i <= m_MaxNumComponents; i++) { MM mixtureModel = buildModel(i, m_Values, m_Weights); double loglikelihood = mixtureModel.loglikelihood(m_Values, m_Weights); if (loglikelihood < loglikelihoodForOneCluster) {
double loglikelihood = model.loglikelihood(values, weights); double[][] probs = new double[model.m_K][values.length]; while (Utils.gr(loglikelihood, oldLogLikelihood)){ loglikelihood = model.loglikelihood(values, weights);
double loglikelihood = model.loglikelihood(values, weights); double[][] probs = new double[model.m_K][values.length]; while (Utils.gr(loglikelihood, oldLogLikelihood)){ loglikelihood = model.loglikelihood(values, weights);